Enabling FPGA and AI Engine Tasks in the HPX Programming Framework for Heterogeneous High-Performance Computing

The increasing complexity of modern exascale computers, with a growing number of cores per node, poses a challenge to traditional programming models. To address this challenge, Asynchronous Many-Task (AMT) runtimes such as the C++-based HPX, divide computational problems into smaller tasks that are executed asynchronously by the runtime. By unifying the syntax and semantics of local and remote task execution, the scalability for distributed execution is enhanced. The asynchronous execution model conceals communication latency in distributed systems and eliminates global synchronization barriers, which improves the overall utilization of computation resources. While HPX and other AMT runtimes often support GPUs, there is still a lack of support for other accelerators, such as FPGAs, or more coarse-grained AI processing elements such as AMD’s AI Engines (AIE). In this work, we extend the TaPaSCo framework so that TaPaSCo FPGA and AIE tasks can be transparently integrated into HPX applications. We show results for both microbenchmarks as well as the complete LULESH proxy HPC application to demonstrate this concept and evaluate the overheads. Both applications show that the combination of TaPaSCo and HPX can be efficiently used for cooperative computing between CPU software and FPGA/AIE hardware. Compared to CPU-only execution, we achieve a speedup of up to 2.4x in our stencil microbenchmark and a wall-clock speedup of 1.37x for the entire LULESH application, with 2.12x in the accelerated kernels itself. Our TaPaSCo/HPX integration is released as open-source.